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Adaptation to Climate Change Adaptation to Climate Change Impact in China Impact in China

• A Case Study on Flood Damage for Investment

Decision-making March 2001

Introduction Objectives Model Structure Scenarios Analysis Conclusion

INTRODUCTION INTRODUCTION

Change in flood risk is frequently cited as

• ne of the potential effects of climate
• change. However, there have still been

relatively few studies on that topic, though there are indications that the frequency of heavy rainfall events is generally likely to increase in some regions with global warming.

Continued...

Attempts have been made to quantify changes in flood occurrence over small areas or from catchment basins. General conclusions are that both the frequency and intensity of floods may increase under changing climate in some seasons from some GCM output and that flood

• ccurrence probability may double by the

end of the next century.

Continued... Continued...

• Mechanisms of adaptation are too complex

to be evaluated. The efficiency of each adaptation strategy has not been sufficiently analyzed quantitatively to propose a detailed action plan, main limitations are derived from the following features of climate change impact and adaptation studies:

• Climate change impact is still uncertain,

and most impacts, even if they occur, will not be significant before the end of the 21st century;

OBJECTIVE OBJECTIVE

Introduce a model that adopts the standard approach of modern optimal economic growth theory and includes two discount factors from the climate sector, i.e., flood damages from climate variability and climate change. Use this model to evaluate the benefits from investment as a robust adaptation strategy in flood prevention infrastructure to adapt to projected climate change impact in China.

MODEL STRUCTURE MODEL STRUCTURE

• Objective Function

Objective Function

∑ ∏

        + × =

− t i t v i

i

t C U Max ) 1 ( ) ) ( ( ρ

The fundamental assumption is that policies should be designed to maximize the generalized level of consumption now and in the future.

U is the flow of utility, Ci(t) is the flow of consumption per capita at year t, ρ is the pure rate of social time preference, i is economic sectors, ν is consumption share of each sector product.

• Production Function

Production Function

      =       = no n t YGDPn n a t Man n n t Mnn t Yn ao a t YGDPa a n t Mna a a t Maa t Ya 2 ) ( , 2 ) ( , 2 ) ( min ) ( 2 ) ( , 2 ) ( , 2 ) ( min ) (

Ya(t) and Yn(t): the gross outputs of the agricultural and the non-agricultural

sectors, respectively;

Maa(t) and Man(t): intermediate inputs from the agricultural sector to both the

agricultural and the non-agricultural sectors, respectively;

Mnn(t) and Mna(t) are intermediate inputs from the non-agricultural sector to both

the non-agricultural and the agricultural sectors, respectively.

YGDPa(t) and YGDPn(t) are the productions of the agricultural and non-agricultural

sectors.

a2a, n2a, n2n, and a2n are input coefficients, and a2ao and n2no are production

factors.

λ

γ β

) ( ) ( ) ( ) ( ) ( t F t La t Ka t Aa t YGDPa × × × =

Aa(t) : total factor of productivity in agricultural sector at year t, Ka(t) : capital input to agricultural sector, La(t) : labor input to agricultural sector, F(t) : land input to agricultural sector, β : elasticity of capital input in agricultural sector, γ : elasticity of labor in agricultural sector, λ : elasticity of farmland in agricultural sector.

Continued... Continued...

α α

) ( ) ( ) ( ) (

1

t Ln t Kn t An t YGDPn × × =

−

α : elasticity of output with respect to capital, An(t) : total factor of productivity in non-agricultural sector, Kn(t) : capital input at year t to agricultural sector, Ln(t) : labor input at year t to agricultural sector.

Continued... Continued...

• Material Balance Constraint

Material Balance Constraint

) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( t IADex t IAex t Iex t Mna t Mnn t IADn t IAn t In t Cn t Yn t Man t Maa t IADa t IAa t Ia t Ca t Ya + + + + + + + + = + + + + + =

Ia(t), IAa(t) and IADa(t) are contributions of the agricultural sector to capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t. In(t), IAn(t) and IADn(t) are contributions of the non-agricultural sector to capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t. Iex(t), IAex(t) and IADex(t) are contributions of the non-agricultural sector to agricultural sector in capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t. Ca(t) and Cn(t) are the consumptions of agricultural and non-agricultural goods, respectively.

• Capital Constraints

Capital Constraints

) ' ( ) ' ( ) ' ( ) ' ( ) ( ) ' ( ) ' ( ) ' ( ) ' ( ) ( ) 1 ( ) 1 ( ) 1 ( ) ( ) 1 ( ) 1 ( ) 1 ( ) 1 ( ) ( t t IADex t t IADn t t IADa t t INRA t INRA t t IAex t t IAn t t IAa t t INR t INR t In t Kn t Kn t Iex t Ia t Ka t Ka − + − + − + − = − + − + − + − = − + − − = − + − + − − = δ δ

δ is the depreciation rate; Ka(t) and Kn(t) are capital stocks of the agricultural and the

non-agricultural sectors, respectively, at year t;

INR(t), INRA(t) are investments to infrastructure which

prevent flooding from current climate variability and projected climate change at year t.

t' is the time lag of investment taking effect.

• Damage Function

Damage Function-

• Climate Variability

Climate Variability

[ ] [ ] [ ] 3

3 2 2 1 1

) ' ( / ) ( 10 ) ( ) ' ( / ) ( 10 ) ( ) ' ( / ) ( 10 ) (

b a b a b a

t t P t INR t DAMn t t P t INR t DAMal t t P t INR t DAMak − = − = − =

DAMn(t), DAMak(t), DAMal(t) are damages to capital stocks

• f the non-agricultural and agricultural sectors, and land at

year t, respectively.

P(t) is population at year t.

a1, a2, a3, b1, b2, b3 are constants that are equivalent to 1.51273, 0.79413, 0.983978, -091843, -077078, -0.35482, respectively.

• Damage Function

Damage Function -

• Climate Change

Climate Change

• Damage Function

Damage Function -

• Climate Change

Climate Change

( ) ( ) ( )

3 3 / 1 3 a3 2 2 / 1 2 a2 1 1 / 1 1 a1

10 / ) ( ) ' ( / ) ( 10 DAMnc(t) 10 / ) ( ) ' ( / ) ( 10 DAMakc(t) 10 / ) ( ) ' ( / ) ( 10 DAMalc(t)

b b a b b a b b a

t Dc t t P t INRA t Dc t t P t INRA t Dc t t P t INRA       + − =       + − =       + − =

DAMnc(t), DAMakc(t), DAMacl(t) are flood damages from

climate change to capital stocks of the non-agricultural and the agricultural sectosr, and land at year t, respectively.

• Annual climate change damage

Annual climate change damage

25 . 6 ) ( ) (

2

t Tc Dref t Dc × =

T(t) is the temperature increase in year t. Damage

caused by flooding under the climate change of a 2.5oC temperature increase is assumed to be Dref, the quadratic term of temperature reflects the assumption that the damage is quadratic along with temperature increase.

• Growth Rate of Technology and Total

Growth Rate of Technology and Total Productivity Factor Productivity Factor

[ ] [ ] [ ] [ ]

) ( exp ) ( ) ( exp ) ( ) ( 1 ) ( ) ( 1 ) ( t GTn ALn t ALn t GTa ALa t ALa t n GTn t GTn t a GTa t GTa

t t

× = × = + × = + × =

− −

ϕ ϕ

φa(t) and φn(t) are the change rates of technology growth for the agricultural and the non-agricultural sectors, GTa(t), GTn(t), GTa0 and GTn0 are the growth rates of technolgoy

• f both sectors at year t and the initial year.

ALa(t), ALn(t), ALa0, ALn0 are total productivity factors of agricultural and non-agricultural sector at year t and initial year

SCENARIOS SCENARIOS

• Climate Change And Investment

Climate Change And Investment

Climate Change No Yes No CnAn CyAn Investment Yes CnAy CyAy

SCENARIOS SCENARIOS

• Population Growth In China

Population Growth In China

Fertility rate (‰) Population in 2000 (billion) Population in 2100 (billion) Low scenario 1.62 1.26 0.8 Medium scenario 1.8 1.26 1.033 High scenario 2.1 1.26 1.5

SCENARIOS SCENARIOS

• Labor Employment In

Labor Employment In Different Sectors Different Sectors

S cenario I II Year 2050 2100 2050 2100

• Agri. S

ector 20% 10% 30% 20% Non-Agri. S ector 80% 90% 70% 80% Labor move rate (%) 0.585

(1995-2050)

0.2

(2051-2100)

0.404

(1995-2050)

0.2

(2051-2100)

Flood damage to cultivated land

BENEFIT ANALYSIS

0.5 1 1.5 2 2.5 3 3.5 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

Year % CnAn CyAn CyAy CnAy

Flood Damage to capital stock of agricultural sector

BENEFIT ANALYSIS

0.5 1 1.5 2 2.5 3 3.5 4 4.5 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

Year % CnAn CyAn CyAy CnAy

Damage to capital stock of non-agricultural sector

BENEFIT ANALYSIS

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

Year %

CnAn CyAn CyAy CnAy

GDP gain of agricultural sector

BENEFIT ANALYSIS

• 0.8
• 0.7
• 0.6
• 0.5
• 0.4
• 0.3
• 0.2
• 0.1

0.1 0.2 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

%

CnAn CyAn CyAy CnAy

GDP Gain of non-agricultural sector

BENEFIT ANALYSIS

• 0.3
• 0.25
• 0.2
• 0.15
• 0.1
• 0.05

0.05 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

%

CnAn CyAn CyAy CnAy

Consumption per capita

BENEFIT ANALYSIS

• 1

.20

• 1

.00

• 0.80
• 0.60
• 0.40
• 0.20

0.00 0.20

% CnAn CyAn CyAy CnAy

Decision making analyses based on consumption change following maximin and maximax principles

Time span Adaptation

• ption

Climate change

• ccurrence

No climate Change

• ccurrence

Minimum benefit Maximum

• f minimum

benefit Best

• ption

Maximum benefit Maximum

• f

maximum benefit Best

• ption

Investment

• 13700.7

422.2

• 13700.7

v 422.2 v 1995-2100 No investment

• 129402.0
• 129402.0
• 13700.7

422.2 Investment

• 6855.3

178.7

• 6855.3

v 178.7 v 1995-2080 No investment

• 34668.6
• 34668.6
• 6855.3

178.7 Investment

• 1534.5
• 51.7
• 1534.5

v

• 51.7

1995-2050 No investment

• 2620.8
• 2620.8
• 1534.5

v

ANALYSIS

Decision making analyses based on consumption change following minimax regret principles

Regret value Time span Adaptation

• ption

Climate change

• ccurrence

No climate change occurrence Maximum regret value Minimum

• f

maximum regret value Best

• ption

Investment v 1995-2100 No investment 115701.3 422.2 115701.3 Investment v 1995-2080 No investment 27813.3 178.7 27813.3 Investment 51.7 51.7 v Consumption 1995-2050 No investment 1086.3 1086.3 51.7

Conclusion Conclusion

Investments optimized ignoring climate change will cause severe damage starting about 2020 when climate change

• happens. Flood damage from climate

change can be effectively mitigated by

• ptimizing investment considering both

climate variability and climate change.

GDP of the agricultural and non- agricultural sectors gains in scenario CnAy when investment considers both climate change and climate variability, even if climate change does not occur

The results show that investment against projected climate change is the best

• ption, whether climate change occurs or
• not. Uncertainty about climate change

should not be an obstacle to formulating an adaptation policy to offset the negative impact of climate change, i.e., floods in this paper. Investment in social infrastructure not only adds adaptive capacity against the impact of future climate change but also improves current society.

The model also is run to test its validity under various combinations of different scenarios of population growth, labor employment in both sectors, and marginal adaptation costs to climate change (increased by 10%, 20% and 30% compared with that to climate variability). The same conclusions can be reached for changes in patterns of GDP and

• consumption. Investment to mitigate

projected climate change is still the best

• ption whether climate change occurs or

not.

Safety Status of Main Rivers/Watershed in China

River/watershed Location Guaranteed Safety Yellow River 1/60 Main streams in middle reaches 1/40 Lower reaches 1/50 Huaihe River Main branches 1/10-1/20 Haihe River & Luanhe River 1/20 Yangtze River Main streams and lakes in middle and lower reaches 1/10-1/20 Taihu Lake & its Surrounding area 1/20 Pearl River Important economic areas 1/50 Other areas 1/10-1/20 Dikes of main streams in Xijiang 1/10-1/20 Dikes of main streams 1/20 Branches 1/10-1/20 Shenyang, Liaoyang, Fushun 1/100 Liaohe River Benxi <1/20 Farmland 1/20 Songhuajiang River Harbin, Qiqihaer, Jiamusi 1/40

Data sources: (1) Liu, 1993, (2) China Agricultural Encyclopedia - Water Conservancy (A). Agricultural Publishing House, 1987, pp 151 Safety standards of infrastructure against flooding area expressed in terms of the frequency of overtopping the flood prevention system.

National Safety Standards of Flood Prevention Infrastructure in China

Standards of flood prevention Cities (Non-agri. pop.: 1000 persons) Mineral Area Cultivated area (1000 ha.) <= 1/200 >= 1500 Very Important > 333.3 1/100~1/200 500~1500 Important 333.3~6.67 1/50~1/100 200~500 Medium 2~6.67 1/20~1/50 <= 200 Less <2

Data source: (1) China Agricultural Encyclopedia - Water Conservancy (A). Agricultural Publishing House, 1987, pp 152; (2) Li, 1997

Flood Damage in China

Year Total damage from natural disasters (billion Yuan) Flood damage (billion Yuan) Flood damage to total damage (%) Percentage equivalent to GDP 1990 61.6 24.0 39.0 1.3 1991 121.6 77.9 64.1 3.6 1992 85.4 41.3 48.4 1.5 1993 99.3 64.2 64.7 1.9 1994 187.6 179.7 95.8 3.9 1995 186.3 165.3 88.7 2.9 1996 288.2 220.8 76.6 3.3 1997 197.5 93 47.1 1.3 1998 307.2 225.1 73.3 2.9 Mean 66.4 2.5

Data source: (1) Outline on water resources statistics (1949-1998). Dept.

• f Planning and Programming, Ministry of Water Resources, PRC. Internal
• Report. (2) Zhang et al., 2000, pp 79. (3) China Disaster Reduction,

(1991~1999).